Search Results for author:
Found 72 papers, 28 papers with code
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance.
Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations
We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.
Distributed Control Design and Safety Verification for Multi-Agent Systems
We propose distributed iterative algorithms for safe control design and safety verification for networked multi-agent systems.
The In-Sample Softmax for Offline Reinforcement Learning
We highlight a simple fact: it is more straightforward to approximate an in-sample \emph{softmax} using only actions in the dataset.
The Cognitive Load of Observation Tasks in 3D Video is Lower Than That in 2D Video
The results showed that 3D videos that required subjects to perform simple tasks brought higher cognitive load to most subjects.
Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments
Autonomous driving systems present promising methods for congestion mitigation in mixed autonomy traffic control settings.
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem.
Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model
To remove the need for an oracle, we first transform the original robust MDPs into an alternative form, as the alternative form allows us to use stochastic gradient methods to solve the robust MDPs.
GWRBoost:A geographically weighted gradient boosting method for explainable quantification of spatially-varying relationships
The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical contexts.
FedSysID: A Federated Approach to Sample-Efficient System Identification
We study the problem of learning a linear system model from the observations of $M$ clients.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind
When reading a story, humans can rapidly understand new fictional characters with a few observations, mainly by drawing analogy to fictional and real people they met before in their lives.
Language Model Pre-Training with Sparse Latent Typing
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks.
Ranked #3 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications
Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics.
A deep variational free energy approach to dense hydrogen
We present a deep generative model-based variational free energy approach to the equations of state of dense hydrogen.
PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection
The temporal information is introduced by the temporal feature aggregation model (TFAM), by conducting an attention mechanism between the context frames and the target frame (i. e., the frame to be detected).
Ask Question First for Enhancing Lifelong Language Learning
Lifelong language learning aims to stream learning NLP tasks while retaining knowledge of previous tasks.
DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation
Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation.
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning
Outsourcing anomaly detection to third-parties can allow data owners to overcome resource constraints (e. g., in lightweight IoT devices), facilitate collaborative analysis (e. g., under distributed or multi-party scenarios), and benefit from lower costs and specialized expertise (e. g., of Managed Security Service Providers).
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost.
RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language Learning
In order to alleviate catastrophic forgetting, we propose the residual variational autoencoder (RVAE) to enhance LAMOL, a recent LLL model, by mapping different tasks into a limited unified semantic space.
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters.
SVR-based Observer Design for Unknown Linear Systems: Complexity and Performance
The variances of the estimation error and the fluctuations in performance are smaller with a properly-designed parameter $\gamma$ compared with the OLS methods.
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation
The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding.
Safety-Aware Optimal Control for Motion Planning with Low Computing Complexity
Simulations demonstrates that BRSCA has a higher probability of finding feasible solutions, reduces the computation time by about 17. 4% and the energy cost by about four times compared to other methods in the literature.
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
Prompt-based learning (i. e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model.
Investigating the Properties of Neural Network Representations in Reinforcement Learning
In this paper we investigate the properties of representations learned by deep reinforcement learning systems.
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation
Federated learning is a framework for distributed optimization that places emphasis on communication efficiency.
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
PromptSource is a system for creating, sharing, and using natural language prompts.
Learning Linear Models Using Distributed Iterative Hessian Sketching
This work considers the problem of learning the Markov parameters of a linear system from observed data.
Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable
The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path.
Reminding the Incremental Language Model via Data-Free Self-Distillation
Incremental language learning with pseudo-data can alleviate catastrophic forgetting in neural networks.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
Robust Glare Detection: Review, Analysis, and Dataset Release
The source of glare is not limited to the sun, and glare can be seen in the images captured during the nighttime and in indoor environments, which is due to the presence of different light sources; reflective surfaces also influence the generation of such artifacts.
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning (MARL) methods with linear or monotonic value decomposition can not ensure the optimal consistency (i. e. the correspondence between the individual greedy actions and the maximal true Q value), leading to instability and poor coordination.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Large-Scale System Identification Using a Randomized SVD
Learning a dynamical system from input/output data is a fundamental task in the control design pipeline.
Visual Enhanced 3D Point Cloud Reconstruction from A Single Image
Solving the challenging problem of 3D object reconstruction from a single image appropriately gives existing technologies the ability to perform with a single monocular camera rather than requiring depth sensors.
3D Object Reconstruction From A Single Image
3D Point Cloud Reconstruction
+1
Entity Resolution in Open-domain Conversations
In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest.
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems
In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved.
Retrieval Enhanced Model for Commonsense Generation
Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts.
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter
In this paper, we present a new dataset named REGRAD for the learning of relationships among objects and grasps.
A Novel Binocular Eye-Tracking SystemWith Stereo Stimuli for 3D Gaze Estimation
However, most current eye trackers focus on 2D point of gaze (PoG) estimation and cannot provide accurate gaze depth. Concerning future applications such as HCI with 3D displays, we propose a novel binocular eye tracking device with stereo stimuli to provide highly accurate 3D PoG estimation.
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.
The Phase Diagram of a Deep Potential Water Model
Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water, from low temperature and pressure to about 2400 K and 50 GPa, excluding the vapor stability region.
Chemical Physics
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
Masked Face Image Classification with Sparse Representation based on Majority Voting Mechanism
Sparse approximation is the problem to find the sparsest linear combination for a signal from a redundant dictionary, which is widely applied in signal processing and compressed sensing.
Enhancing Generalization in Natural Language Inference by Syntax
Pre-trained language models such as BERT have achieved the state-of-the-art performance on natural language inference (NLI).
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory
We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functional theory.
Online Visual Place Recognition via Saliency Re-identification
As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving.
Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high computational cost.
Computational Physics Materials Science Chemical Physics
SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding
Our model, SentPWNet, exploits the neighboring spatial distribution of each sentence as locality weight to indicate the informative level of sentence pair.
Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles.
Computational Physics
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM).
Deep Attention Aware Feature Learning for Person Re-Identification
Visual attention has proven to be effective in improving the performance of person re-identification.
Ranked #7 on
Person Re-Identification
on Market-1501
Fast Loop Closure Detection via Binary Content
Our method is compared with the state-of-the-art loop closure detection methods and the results show that it outperforms the traditional methods at both recall rate and speed.
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Materials 3, 023804] and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training.
Computational Physics
VideoDP: A Universal Platform for Video Analytics with Differential Privacy
In this paper, to the best of our knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee.
Deep neural network for Wannier function centers
We introduce a deep neural network (DNN) model that assigns the position of the centers of the electronic charge in each atomic configuration on a molecular dynamics trajectory.
Computational Physics Materials Science Chemical Physics
Machine learning-guided synthesis of advanced inorganic materials
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development.
Learning to Decompose Compound Questions with Reinforcement Learning
Our model consists of two parts: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) three independent simple-question answerers that classify the corresponding relations for each simple question.
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials.
Learn to Steer through Deep Reinforcement Learning
It is worth noting that the developed system is readily transferable from virtual training scenarios to real-world deployment without any fine-tuning by utilizing depth images.
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples
Adversarial machine learning in the context of image processing and related applications has received a large amount of attention.
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology.
Computational Physics Materials Science Chemical Physics
English Out-of-Vocabulary Lexical Evaluation Task
Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge.
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics.
Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Like metadynamics, it allows for an efficient exploration of the configuration space by adding an adaptively computed biasing potential to the original dynamics.
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data.
Detecting facial landmarks in the video based on a hybrid framework
Finally, the facial landmarks in the current frame are exactly detected from the validated face bounding box via the landmark detector.
Time-domain multiscale shape identification in electro-sensing
This paper presents premier and innovative time-domain multi-scale method for shape identification in electro-sensing using pulse-type signals.
Wavelet methods for shape perception in electro-sensing
This paper aims at presenting a new approach to the electro-sensing problem using wavelets.
